Corporations, institutions, and governments spend hundreds of millions of dollars each year to digitize documents, films, maps, books, and other physical media. Included in this mix are billions of pages of medical records, legal evidence, corporate documents, material from national and regional archives, and banking checks. The resulting digital image files represent valuable information whose accuracy and security have significance in current working operations and for long-term archiving. The digitization process is the gateway for this information onto networked systems, which allows for convenient, cost effective, and efficient transmission, storage, searching, and retrieval of the image information. The demand for digital scanning of physical media has increased dramatically in the last few years because of large improvements in communication bandwidth and digital storage capabilities of such systems.
This surge in the digitization of physical media is exemplified by the announcement by Google Inc. on 14 Dec. 2005 that it is working with the libraries of Harvard, Stanford, University of Michigan, and University of Oxford, as well as The New York Public Library, to digitally scan books from their collections so that users around the world can access them using the Google search engine. As another example, the Check Clearing for the 21st Century Act (“Check 21”), which was signed into law and became effective on Oct. 28, 2004, allows banks to move checks electronically, rather than as physical documents, in order to make the check clearance process faster and more efficient. Banks can scan the front and back of a check and then transmit this image data and payment information in lieu of shipping the original check. If a paper check is required, the bank can use the image data and payment information to create a paper “substitute check.” Banks are not required to keep the original check, and it is typically destroyed or “truncated” to reduce maintenance costs.
Organizations also spend vast amounts of money on capturing day-to-day activities with digital image capture devices, such as inspection cameras for manufacturing processes, forensic crime scene cameras, in-car police cameras, automated teller machine (ATM) cameras, and surveillance cameras for monitoring facilities, equipment, and personnel. Some of these applications use computer vision techniques to automatically analyze the images for certain features or events. In many cases, the images that are produced by these devices are never viewed by a human being unless a specific event triggers a review. Regardless of whether the images are analyzed by computers or viewed by humans, it is essential that the image data represent the physical scene with sufficient fidelity for the intended application.
Because of the sensitive nature of the information in many applications, it is important to ensure that the image data is not tampered with after it is generated. It is a simple matter to change the contents of a digital image by using an image editor or other readily available computer technology. Increased awareness of security and privacy issues is resulting in national and international legislation that addresses such concerns about tampering. For example, the Canadian government recently began a program to provide public Internet access to its heritage or historical repositories. Fears about the possible tampering of the government data compelled the Canadian Parliament to require that the controlling organizations make “reasonable” attempts to ensure the integrity of their scanned images.
One approach to ensuring data integrity is to use encryption. However, encryption can be computationally expensive for large amounts of data, such as is the case for high resolution images and video sequences. As a result, a more practical approach to ensuring the integrity of a digital data file is to use a digital signature. Digital signatures are based on the concept of a hash. A hash is a relatively short numerical value that represents a distilled version of the larger digital data file. Methods that perform this distillation are referred to as hash functions or hash algorithms, and they are used widely in computer systems. Hash functions are designed so that a small change in the digital data file will produce a significant change in the calculated hash value. A digital signature is an encrypted version of the hash, typically using a public-key infrastructure (PKI) algorithm, and the digital signature is associated with the digital file in some way, such as attaching it to the file header or storing in a database that is indexed by the filename or other unique identifier. In this way, any tampering with the digital data can be detected by recalculating the hash and comparing it to the original hash in the secure digital signature. Any discrepancy between the two hash values indicates tampering with the digital data in the file. An image that has been associated with a digital signature in the manner just described is often called a “secure” image. A benefit of securing images with digital signatures is that the image data itself is in the “clear”, that is, unencrypted, which means it can be used like any other image, yet its integrity can be verified at any time.
While these approaches allow the integrity of image data to be verified, they do not address the issue of the quality of the image data. Image quality is determined by many factors, including such attributes as resolution, sharpness, dynamic range, noise, and color reproduction. The digital image data that represents a physical medium or scene could be meaningless, erroneous, or artifact-laden for a variety of reasons, such as a defective scanner or a camera that is out of focus, for example. In such cases, the techniques for authenticating data as described previously may be of limited value because they can be protecting data that is worthless.
The knowledge that image data is a satisfactory replica of an original physical medium or scene is clearly important. Companies that are responsible for the scanning of important documents for governments, financial institutions, and other concerns may become liable for loss of valuable information if the scanned image quality is insufficient and the original physical documents have been destroyed. For example, banks that scan checks to produce electronic records under Check 21 are liable for any financial losses associated with poor quality images. Even if the original documents are still available, significant costs may incurred in rescanning. End users of the scanned documents may also be affected by poor scan quality because of a diminished ability to extract or interpret the information that was contained in the original document. Likewise, law enforcement agencies may be hampered in their identification and prosecution of criminals if surveillance video images or forensic still images have insufficient quality.
In a Check 21 environment, image quality is typically assessed at the point of image capture, and the image quality affects the workflow of the electronic check data. For example, a poor quality image may require special handling, which incurs extra costs. A bank that receives a poor quality check image might require the originating bank to rescan the check, or the receiving bank might simply assume liability for the cost of the check if it is a small dollar amount. The result is an increase in service costs and delays in completing the clearance of checks, as well as the potential loss of good will with customers. Thus, there is a significant value associated with the ability to properly assess image quality.
There are various ways to assess image quality. One approach is to have a trusted human being review an image for image quality before submitting the image data to a secure hash algorithm to establish data integrity. However, given the tremendous number of images that are produced daily, a human-based quality control solution is not economically viable in many applications. In addition, human error rates may be significant and may exceed the threshold of customer tolerance.
Another approach to assessing image quality is to use test targets. A test target acts as a reference image, and the quality metrics calculated from that reference can provide measures of actual versus ideal performance for a capture device. Quality measurements using known test targets are termed “full reference” measurements. Test targets are often used on an intermittent basis during the operation of an image capture device to determine if the device is performing as expected. However, the intermittent use of test targets doesn't necessarily provide information about the image quality that is achieved for the capture of a particular physical medium or scene. One reason for this is the actual physical medium or scene may have unique imaging properties as compared to the test target, potentially leading to reduced quality even if the test target image quality is acceptable. For example, an adaptive image processing algorithm that automatically controls image brightness and contrast might not produce the optimal code values for the image data because of the background color in the image. Another reason could be mechanical malfunction, such as when a document feed mechanism fails to place a document properly on the scanning platen or when two documents are inadvertently piggybacked together. It is possible for these failures to occur only sporadically, and the test target images may not suffer from such failures. In some applications, it may be possible to include a test target in every image that is captured by a device, but this can be costly and is often impractical. Moreover, it still may be the case that quality of the captured medium or scene is not fully reflected in the quality that is determined from the included test target data, for reasons such as those described previously.
A third approach is to assess image quality directly from the captured image data itself. When the only information that is available to assess quality is the image data, which generally has unknown characteristics, the quality measurement techniques are referred to as “no-reference” methods. An example of a no-reference image quality metric is described in a technical paper entitled “A no-reference perceptual blur metric” by P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, Proceedings of the IEEE International Conference on Image Processing, Vol. III, pp. 57-60, September 2002. The method in this paper computes a blur metric (that is, a loss in sharpness) by identifying vertical edges in an image and then determining the average spatial extent of the edges. The Financial Services Technology Consortium (FSTC), which is a consortium of banks, financial services providers, academic institutions, and government agencies, has investigated a similar no-reference blur metric for Check 21 applications. The FSTC has also investigated a number of other no-reference quality metrics for Check 21 applications, including compressed image file size, document skew angle, and number of black pixels (for a bi-tonal image). A full description of the FSTC quality metrics can be found at the FSTC web site.
Regardless of the method that is used to assess image quality, it is also necessary to have the image quality measures secured against possible tampering because of the previously discussed economic, liability, and legal issues that are associated with image quality. Moreover, at various points in the lifecycle of a digital image, it may be desirable to check quickly on the image quality without having to perform another visual inspection or computer analysis of the image data. This capability can be achieved by assessing image quality once, typically at the point of capture, and then securing the quality metrics against tampering. Furthermore, it is desirable to have the secure image quality measures and the secure image data inseparably linked, so that any change in the image data renders the associated quality metrics as invalid. Current applications that assess image quality, such as Check 21 processing systems, do not secure the image quality metrics and hence are susceptible to tampering of the quality data, which may result in an inefficient workflow and financial losses. It is easy to imagine that a digital scan of a check may be vulnerable to courtroom challenge on the basis of poor image quality, despite the use of digital signatures for the image data itself by the bank. With secure image quality measures, the liabilities of those parties who are responsible for the scanned data can be minimized.
In a commonly assigned U.S. Pat. No. 7,706,567 entitled “Assured document and method of making” by Robert J. McComb, filed 16 May 2006, a method is taught for assessing the scanned image quality of documents using test targets and for securing the image quality assessment in combination with secure image data. The document images that are produced by this method are termed “assured documents”. Image quality metrics are calculated from test targets that are periodically inserted into a document queue, and these metrics are associated with the scanned image data for user documents that were in the same document queue. The quality metrics are associated with the image data of an individual user document by combining the quality metrics with a secure hash value that represents a distillation of the image data, followed by encryption of the combined quality metrics and hash value. The encrypted quality metrics and hash value are stored in the file header or filename of the digital document, or by other means, as disclosed in the McComb patent.
While the method of McComb is aimed primarily at document scanning applications, it is clear that the method of securing image quality metrics and associating the secure quality metrics with the image data has broader applicability to other imaging applications. However, relying on test targets for assessing image quality can be constraining, particularly where test targets may not be readily available or usable. For example, placing a test target into every scene that is captured with a rotating surveillance camera is obviously impractical. Moreover, even if test targets were practical in an application, the production of the test targets and the need for a mechanism (either automated or manual) for inserting the test targets into an image capture workflow can add considerable complication and expense.
Thus, there is the need for a method to efficiently assess the image quality of image data on an individual image basis, without relying strictly on the use of test targets, and to securely associate the image quality with the image data, while also ensuring the integrity of the image data.